




'''simple docstring'''







import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        FalconForCausalLM,
        FalconForQuestionAnswering,
        FalconForSequenceClassification,
        FalconForTokenClassification,
        FalconModel,
    )

class 	_snake_case:






 def __init__(self       :  List[Any]    ,		a       :  Tuple    ,		a       :  int=3    ,		a       :  List[str]=7    ,		a       :  str=True    ,		a       :  List[str]=True    ,		a       :  int=False    ,		a       :  Any=True    ,		a       :  Tuple=99    ,		a       :  Optional[Any]=32    ,		a       :  List[Any]=5    ,		a       :  Optional[int]=4    ,		a       :  int=37    ,		a       :  Any="gelu"    ,		a       :  str=0.1    ,		a       :  List[Any]=0.1    ,		a       :  Dict=5_12    ,		a       :  Any=16    ,		a       :  Optional[int]=2    ,		a       :  Union[str, Any]=0.02    ,		a       :  Any=3    ,		a       :  Dict=4    ,		a       :  str=None    ,		)       ->   Tuple:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     seq_length
    A__       =     is_training
    A__       =     use_input_mask
    A__       =     use_token_type_ids
    A__       =     use_labels
    A__       =     vocab_size
    A__       =     hidden_size
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     hidden_act
    A__       =     hidden_dropout_prob
    A__       =     attention_probs_dropout_prob
    A__       =     max_position_embeddings
    A__       =     type_vocab_size
    A__       =     type_sequence_label_size
    A__       =     initializer_range
    A__       =     num_labels
    A__       =     num_choices
    A__       =     scope






 def 						_UpperCamelCase (self       :  Dict    )       ->   Dict:






    """simple docstring"""


    A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )

    A__       =     None
    if self.use_input_mask:
       A__       =     random_attention_mask([self.batch_size, self.seq_length]    )

    A__       =     None

    A__       =     None
    A__       =     None
    A__       =     None
    if self.use_labels:
       A__       =     ids_tensor([self.batch_size]    ,		self.type_sequence_label_size    )
       A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels    )
       A__       =     ids_tensor([self.batch_size]    ,		self.num_choices    )

    A__       =     self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






 def 						_UpperCamelCase (self       :  Dict    )       ->   Optional[Any]:






    """simple docstring"""


    return FalconConfig(
        vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		is_decoder=a    ,		initializer_range=self.initializer_range    ,		pad_token_id=1    ,		new_decoder_architecture=a    ,		)






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Optional[int]    ,		a       :  List[str]    ,		a       :  Optional[Any]    ,		a       :  Optional[int]    ,		a       :  List[str]    ,		a       :  List[str]    ,		a       :  Optional[Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     FalconModel(config=a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		attention_mask=a    )
    A__       =     model(a    )
    self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  List[str]    ,		a       :  str    ,		a       :  List[Any]    ,		a       :  Optional[Any]    ,		a       :  Tuple    ,		a       :  Optional[int]    ,		a       :  Any    ,		a       :  Tuple    ,		a       :  Tuple    ,		)       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     True
    A__       =     FalconModel(a    )
    model.to(a    )
    model.eval()
    A__       =     model(
        a    ,		attention_mask=a    ,		encoder_hidden_states=a    ,		encoder_attention_mask=a    ,		)
    A__       =     model(
        a    ,		attention_mask=a    ,		encoder_hidden_states=a    ,		)
    A__       =     model(a    ,		attention_mask=a    )
    self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Any    ,		a       :  Tuple    ,		a       :  Tuple    ,		a       :  List[Any]    ,		a       :  List[str]    ,		a       :  Any    ,		a       :  Union[str, Any]    ,		a       :  List[Any]    ,		a       :  Dict    ,		)       ->   Any:






    """simple docstring"""


    A__       =     FalconForCausalLM(config=a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		attention_mask=a    ,		labels=a    )
    self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)    )






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  Any    ,		a       :  List[str]    ,		a       :  List[str]    ,		a       :  Optional[int]    ,		a       :  int    ,		a       :  Optional[int]    ,		a       :  Union[str, Any]    ,		a       :  Dict    ,		a       :  List[str]    ,		)       ->   Tuple:






    """simple docstring"""


    A__       =     True
    A__       =     True
    A__       =     FalconForCausalLM(config=a    )
    model.to(a    )
    model.eval()

    # first forward pass
    A__       =     model(
        a    ,		attention_mask=a    ,		encoder_hidden_states=a    ,		encoder_attention_mask=a    ,		use_cache=a    ,		)
    A__       =     outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    A__       =     ids_tensor((self.batch_size, 3)    ,		config.vocab_size    )
    A__       =     ids_tensor((self.batch_size, 3)    ,		vocab_size=2    )

    # append to next input_ids and
    A__       =     torch.cat([input_ids, next_tokens]    ,		dim=-1    )
    A__       =     torch.cat([input_mask, next_mask]    ,		dim=-1    )

    A__       =     model(
        a    ,		attention_mask=a    ,		encoder_hidden_states=a    ,		encoder_attention_mask=a    ,		output_hidden_states=a    ,		)['hidden_states'][0]
    A__       =     model(
        a    ,		attention_mask=a    ,		encoder_hidden_states=a    ,		encoder_attention_mask=a    ,		past_key_values=a    ,		output_hidden_states=a    ,		)['hidden_states'][0]

    # select random slice
    A__       =     ids_tensor((1,)    ,		output_from_past.shape[-1]    ).item()
    A__       =     output_from_no_past[:, -3:, random_slice_idx].detach()
    A__       =     output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(a    ,		a    ,		atol=1e-3    )    )






 def 						_UpperCamelCase (self       :  str    )       ->   Any:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    (
        (
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			
    )       =     config_and_inputs
    A__       =     {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict




@require_torch
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Optional[Any]						=					(
     (
         FalconModel,
         FalconForCausalLM,
         FalconForSequenceClassification,
         FalconForTokenClassification,
         FalconForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __snake_case:							Union[str, Any]						=					(FalconForCausalLM,) if is_torch_available() else ()
 __snake_case:							List[str]						=					(
     {
         '''feature-extraction''': FalconModel,
         '''text-classification''': FalconForSequenceClassification,
         '''text-generation''': FalconForCausalLM,
         '''question-answering''': FalconForQuestionAnswering,
         '''token-classification''': FalconForTokenClassification,
         '''zero-shot''': FalconForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __snake_case:							List[str]						=					False
 __snake_case:							Union[str, Any]						=					False






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     FalconModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		hidden_size=37    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   int:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Any:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Dict:






    """simple docstring"""


    A__  ,			*A__       =     self.model_tester.prepare_config_and_inputs()
    for alibi in [True, False]:
       A__       =     alibi
       self.model_tester.create_and_check_model(a    ,		*a    )






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Optional[int]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     3
    A__       =     input_dict['input_ids']
    A__       =     input_ids.ne(1    ).to(a    )
    A__       =     ids_tensor([self.model_tester.batch_size]    ,		self.model_tester.type_sequence_label_size    )
    A__       =     FalconForSequenceClassification(a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		attention_mask=a    ,		labels=a    )
    self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels)    )






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Optional[int]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     3
    A__       =     'single_label_classification'
    A__       =     input_dict['input_ids']
    A__       =     input_ids.ne(1    ).to(a    )
    A__       =     ids_tensor([self.model_tester.batch_size]    ,		self.model_tester.type_sequence_label_size    )
    A__       =     FalconForSequenceClassification(a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		attention_mask=a    ,		labels=a    )
    self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels)    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   List[Any]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     input_dict['input_ids']
    A__       =     FalconForCausalLM(a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		use_cache=a    )
    A__       =     input_ids.shape[0]
    A__       =     model._convert_to_rw_cache(result.past_key_values    )
    A__       =     model._convert_cache_to_standard_format(a    ,		a    )
    for layer in range(len(a    )    ):
       for tensor_idx in range(2    ):
          self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3    )
          self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4    )
          self.assertTrue(
              torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]    )    )






 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[int]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     3
    A__       =     'multi_label_classification'
    A__       =     input_dict['input_ids']
    A__       =     input_ids.ne(1    ).to(a    )
    A__       =     ids_tensor(
        [self.model_tester.batch_size, config.num_labels]    ,		self.model_tester.type_sequence_label_size    ).to(torch.float    )
    A__       =     FalconForSequenceClassification(a    )
    model.to(a    )
    model.eval()
    A__       =     model(a    ,		attention_mask=a    ,		labels=a    )
    self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels)    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Any:






    """simple docstring"""


    for model_class in self.all_generative_model_classes:
       A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

       # If it doesn't support cache, pass the test
       if not hasattr(a    ,		'use_cache'    ):
          return

       A__       =     model_class(a    ).to(a    )
       if "use_cache" not in inputs:
          A__       =     True
       A__       =     model(**a    )

       # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
       if "past_key_values" not in outputs:
          return

       A__       =     (
           getattr(a    ,		'decoder_layers'    ,		a    )
           or getattr(a    ,		'num_decoder_layers'    ,		a    )
           or config.num_hidden_layers
       )
       A__       =     getattr(a    ,		'num_kv_heads'    ,		config.num_attention_heads    )
       A__       =     getattr(a    ,		'd_model'    ,		config.hidden_size    )
       A__       =     embed_dim // num_attention_heads

       A__       =     outputs['past_key_values']
       self.assertEqual(len(a    )    ,		a    )

       A__  ,			A__       =     inputs['input_ids'].shape
       for i in range(a    ):
          if config.new_decoder_architecture:
             A__       =     config.num_attention_heads
          elif config.multi_query:
             A__       =     1
          self.assertEqual(len(past_kv[0]    )    ,		2    )  # K V for the decoder = 2
          self.assertEqual(
              past_kv[i][0].shape    ,		(batch_size, num_attention_heads, seq_length, per_head_embed_dim)    )
          self.assertEqual(
              past_kv[i][1].shape    ,		(batch_size, num_attention_heads, seq_length, per_head_embed_dim)    )




@require_torch
class 	_snake_case(			unittest.TestCase      ):






 @slow
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    A__       =     AutoTokenizer.from_pretrained('Rocketknight1/falcon-rw-1b'    )
    A__       =     FalconForCausalLM.from_pretrained('Rocketknight1/falcon-rw-1b'    )
    model.eval()
    model.to(a    )
    A__       =     tokenizer('My favorite food is'    ,		return_tensors='pt'    ).to(a    )

    A__       =     (
        'My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'
    )

    A__       =     model.generate(**a    ,		do_sample=a    ,		max_new_tokens=19    )
    A__       =     tokenizer.batch_decode(a    )[0]

    self.assertEqual(a    ,		a    )






 @slow
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   List[str]:






    """simple docstring"""


    for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
       A__       =     AutoTokenizer.from_pretrained(a    )
       A__       =     FalconForCausalLM.from_pretrained(a    )
       model.eval()
       model.to(a    )
       A__       =     tokenizer('My favorite food is'    ,		return_tensors='pt'    ).to(a    )

       # We just test that these run without errors - the models are randomly initialized
       # and so the actual text outputs will be garbage
       model.generate(**a    ,		do_sample=a    ,		max_new_tokens=4    )
       model.generate(**a    ,		do_sample=a    ,		max_new_tokens=4    )
       model.generate(**a    ,		num_beams=2    ,		max_new_tokens=4    )






 @slow
 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    with torch.no_grad():
       for repo in [
           "Rocketknight1/falcon-rw-1b",
           "Rocketknight1/tiny-random-falcon-7b",
           "Rocketknight1/tiny-random-falcon-40b",
       ]:
          A__       =     AutoTokenizer.from_pretrained(a    )
          A__       =     FalconForCausalLM.from_pretrained(a    )
          model.eval()
          model.to(device=a    )
          A__       =     tokenizer('My favorite food is'    ,		return_tensors='pt'    ).to(a    )

          # Test results are the same with and without cache
          A__       =     model.generate(**a    ,		do_sample=a    ,		max_new_tokens=20    ,		use_cache=a    )
          A__       =     model.generate(**a    ,		do_sample=a    ,		max_new_tokens=20    ,		use_cache=a    )
          self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0    )








'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						).convert('RGB'						)

   return image







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # fmt: off

   # vision encoder
   rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')						)
   rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')						)
   rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')						)
   rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')						)

   for i in range(config.vision_config.num_hidden_layers						):
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")						)

   # QFormer
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')						)
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')						)

   # fmt: on
   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   for i in range(config.vision_config.num_hidden_layers						):
      # read in original q and v biases
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""						)
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""						)

      # next, set bias in the state dict
      A__       =     torch.cat((q_bias, torch.zeros_like(UpperCAmelCase      ,requires_grad=UpperCAmelCase						), v_bias)						)
      A__       =     qkv_bias







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     364 if 'coco' in model_name else 224
   A__       =     InstructBlipVisionConfig(image_size=UpperCAmelCase						).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "t5-xl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xxl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'      ,vocab_size=32001						).to_dict()
   elif "vicuna-13b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'      ,vocab_size=32001						).to_dict()
   else:
      raise ValueError('Model name not supported'						)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   A__       =     InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
   A__       =     InstructBlipConfig(vision_config=UpperCAmelCase      ,text_config=UpperCAmelCase      ,qformer_config=UpperCAmelCase						)

   return config, image_size







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-uncased'      ,truncation_side='left'						)
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}						)

   if "t5" in model_name:
      A__       =     TaTokenizerFast.from_pretrained('google/flan-t5-xl'      ,truncation_side='left'						)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      A__       =     LlamaTokenizerFast.from_pretrained(
          'huggyllama/llama-7b'      ,truncation_side='left'      ,bos_token='</s>'      ,unk_token='</s>'						)
      tokenizer.add_special_tokens({'pad_token': '[PAD]'}						)

   A__  ,			A__       =     get_blipa_config(UpperCAmelCase						)
   A__       =     InstructBlipForConditionalGeneration(UpperCAmelCase						).eval()

   A__       =     {
       'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
       'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
       'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
       'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
   }

   A__  ,			A__       =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'						)
   A__       =     'cuda:1' if torch.cuda.is_available() else 'cpu'
   A__       =     'cuda:2' if torch.cuda.is_available() else 'cpu'
   A__  ,			A__  ,			A__       =     load_model_and_preprocess(
       name=UpperCAmelCase      ,model_type=UpperCAmelCase      ,is_eval=UpperCAmelCase      ,device=UpperCAmelCase						)
   original_model.eval()
   print('Done!'						)

   # update state dict keys
   A__       =     original_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      A__       =     state_dict.pop(UpperCAmelCase						)
      if key.startswith('Qformer.bert'						):
         A__       =     key.replace('Qformer.bert'      ,'qformer'						)
      if "attention.self" in key:
         A__       =     key.replace('self'      ,'attention'						)
      if "llm_proj" in key:
         A__       =     key.replace('llm_proj'      ,'language_projection'						)
      if "t5_proj" in key:
         A__       =     key.replace('t5_proj'      ,'language_projection'						)
      if key.startswith('llm_model'						):
         A__       =     key.replace('llm_model'      ,'language_model'						)
      if key.startswith('t5'						):
         A__       =     key.replace('t5'      ,'language'						)
      A__       =     val

   # read in qv biases
   read_in_q_v_bias(UpperCAmelCase      ,UpperCAmelCase						)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(UpperCAmelCase      ,strict=UpperCAmelCase						)

   A__       =     load_demo_image()
   A__       =     'What is unusual about this image?'

   # create processor
   A__       =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}      ,image_mean=UpperCAmelCase      ,image_std=UpperCAmelCase						)
   A__       =     InstructBlipProcessor(
       image_processor=UpperCAmelCase      ,tokenizer=UpperCAmelCase      ,qformer_tokenizer=UpperCAmelCase      ,)
   A__       =     processor(images=UpperCAmelCase      ,text=UpperCAmelCase      ,return_tensors='pt'						).to(UpperCAmelCase						)

   # make sure processor creates exact same pixel values
   A__       =     vis_processors['eval'](UpperCAmelCase						).unsqueeze(0						).to(UpperCAmelCase						)
   A__       =     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device						)      ,UpperCAmelCase						)

   original_model.to(UpperCAmelCase						)
   hf_model.to(UpperCAmelCase						)
   with torch.no_grad():
      if "vicuna" in model_name:
         A__       =     original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
         A__       =     hf_model(**UpperCAmelCase						).logits
      else:
         A__       =     original_model(
             {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}						).logits
         A__       =     tokenizer('\n'      ,return_tensors='pt'						).input_ids.to(UpperCAmelCase						)
         A__       =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,-100						)
         A__       =     hf_model(**UpperCAmelCase      ,labels=UpperCAmelCase						).logits

   print('First values of original logits:'      ,original_logits[0, :3, :3]						)
   print('First values of HF logits:'      ,logits[0, :3, :3]						)

   # assert values
   assert original_logits.shape == logits.shape
   A__       =     1e-4 if 'vicuna' in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device						)      ,UpperCAmelCase      ,atol=UpperCAmelCase						)
   print('Looks ok!'						)

   print('Generating with original model...'						)
   A__       =     original_model.generate({'image': original_pixel_values, 'prompt': prompt}      ,num_beams=5						)

   # important: we need to cast the weights of the HF model to the appropriate type
   print('Generating with HF model...'						)
   A__       =     hf_model.generate(
       **UpperCAmelCase      ,do_sample=UpperCAmelCase      ,num_beams=5      ,max_length=256      ,min_length=1      ,top_p=0.9      ,repetition_penalty=1.5      ,length_penalty=1.0      ,temperature=1      ,)
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      A__       =     2
   print('Original generation:'      ,UpperCAmelCase						)
   A__       =     processor.batch_decode(UpperCAmelCase      ,skip_special_tokens=UpperCAmelCase						)
   A__       =     [text.strip() for text in output_text]
   print('HF generation:'      ,UpperCAmelCase						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      processor.push_to_hub(F"""Salesforce/{model_name}"""						)
      hf_model.push_to_hub(F"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    lowerCAmelCase_     						=				[
        '''instructblip-vicuna-7b''',
        '''instructblip-vicuna-13b''',
        '''instructblip-flan-t5-xl''',
        '''instructblip-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''instructblip-flan-t5-xl''',
        choices=choices,
        type=str,
        help='''Path to hf config.json of model to convert''',
    )
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether to push the model and processor to the hub after converting''',
    )

    lowerCAmelCase_     						=				parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


